^ Groutability Prediction of Microfine Cement Based Soil Improvement Using Evolutionary Ls-svm Inference Model | Structurae
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Groutability Prediction of Microfine Cement Based Soil Improvement Using Evolutionary Ls-svm Inference Model

Author(s):

Medium: journal article
Language(s): Latvian
Published in: Journal of Civil Engineering and Management, , n. 6, v. 20
Page(s): 839-848
DOI: 10.3846/13923730.2013.802717
Abstract:

Permeation grouting is a widely used technique for soil improvement in construction engineering. Thus, predicting the results of the grouting activity is a particularly interesting topic that has drawn the attention of researchers both from the academic field and industry. Recent literature has indicated that artificial intelligence (AI) approaches for groutability prediction are capable of delivering better performance than traditional formula-based ones. In this study, a novel AI method, evolutionary Least Squares Support Vector Machine Inference Model for groutability prediction (ELSIM-GP), is proposed to forecast the result of grouting activity that utilizes microfine cement grout. In the model, Least Squares Support Vector Machine (LS-SVM) is a supervised machine learning technique that is employed to learn the decision boundary for classifying high dimensional data. Differential Evolution (DE) is integrated into ELSIM-GP for automatically optimizing its tuning parameters. 240 historical cases of grouting process for sandy silt soil have been collected to train, validate, and test the inference model. Experimental results demonstrated that ELSIM-GP can overcome other benchmark approaches in terms of forecasting accuracy. Therefore, the proposed approach is a promising alternative for predicting groutability.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.3846/13923730.2013.802717.
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  • Reference-ID
    10354571
  • Published on:
    13/08/2019
  • Last updated on:
    13/08/2019